Edward Conard

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Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio

Carol Corrado Intelligence
Date Posted:
October 26, 2021
Is Database:
Database

AI investments have not translated into sustained TFP growth post-financial crisis, despite significant unmeasured investment in AI-related intangible assets. @CarolCorrado

Despite significant unmeasured investment in AI-related intangible assets, such as design and training, there is little evidence of a 'J-curve' effect on total factor productivity (TFP) growth. The anticipated upward 'swoosh' in TFP growth, which would indicate long-lasting returns from these investments, is not evident. Data from the US and EU show that while intangible investment has increased, particularly in the US, it has not translated into sustained TFP growth post-financial crisis. The high depreciation rates of intangibles mean that any positive impact on TFP growth appears quickly but is insufficient to counteract the slowdown observed. This suggests that current AI investments may not be significantly mismeasured or that we are still early in the AI investment cycle to detect substantial effects.

Carol Corrado on the potential impact of AI on productivity, basically fails to find support for the J curve camp, key quote, "... Our main finding is that, on our data at least, there is indeed plenty of unmeasured investment but little sign of a ‘J-curve’ effect on TFP growth. The upward ‘swoosh’ of the effects of investments whose returns are long-lasting just is not there..."
Core of paper, "...We have set out a framework to illustrate this effect and used a cross-country-industry-year data set for the US and European economies to examine it. To look for unmeasured investment, we have used the CHS approach, which brings into national accounts unmeasured investment in intangible assets which are likely complementary to artificial intelligence, such as design, training, and business process re-engineering. We have also seen that at least some artificial intelligence investment is likely in software investment and is thus already counted. Thus we have harmonized the deflation of software and hardware investment to facilitate comparison across different countries. At least on these data, we do not find much support for the J-curve view. There is, indeed, plenty of unmeasured investment, but the trend in such investment does not seem to be sufficient to give an effect. In particular, the high depreciation rates mean that the missing capital payments which bias TFP growth up follow quickly after a burst of intangible investment, meaning that the upward-sloping part of the J curve, at least on our data, appears very quickly, too quickly to account for a sustained TFP growth slowdown following the financial crisis. That said, we are in the early stages of measuring AI, and since much of it is taking place within firms on their own account, the detection of such investment is extremely difficult..."

The evidence

"...Figure 8 shows shares of tangible and intangible investment across countries from 1997 to 2017. As the graph shows, intangible investment is generally trended upwards and the US invests considerably more intangible investment then the EU. This immediately suggested accounting for intangible investment might potentially be important...."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 1


"...As we have discussed, national accounts do indeed measure intangible investment. Figure 9 plots national accounts intangible investment and the additional investment suggested by CHS set out in Table 2. As the graph shows, the non-national accounts intangible share is higher than the national accounts intangible share, reflecting the fact that expenditure on items such as training and design are large. However, the trend is towards relatively more intensive national accounts intangibles measurement..."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 2


"...Finally, Figure 10 shows growth in real investment for tangibles, intangibles, and software. Notice that, particularly in the US, software spending grows particularly strongly at the end of the period. If artificial intelligence is included in such spending this is suggestive. The position seems much more volatile in Europe..."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 3


"...To start our examination of total factor productivity,Figure 11 shows growth accounting for the EU and US for the years 1997-2007 and 2010-17 (we omit the recession years of 2008 and 2009). As the top panel shows, labour productivity and TFP were growing at a healthy rate in the run up to the financial crisis. The contributions of both tangible and intangible capital deepening were higher in the US than in Europe, and their contributions exceeded those of labour composition. In the period following the financial crisis, the situation changed. As is well known, labour productivity growth slowed substantially by around 0.5 percentage points per annum (pppa) in Europe and 2pppa in the US. US TFP growth also slowed: if anything European TFP growth was around the same. Intangible capital deepening slowed very strongly in the US, and somewhat in Europe...."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 4


"... Figure 12 shows the biases to measured TFP growth for the EU and the US respectively: these are the terms on the right of equation (4), second line. A positive number indicates that measured TFP growth is too big. Recall that the ‘J curve’ hypothesis is that measured TFP growth after the financial crisis is too small and then rises, suggesting that after the financial crisis the bias line should dip and then perhaps rise. At least for the EU, there is not much support for this hypothesis. Note first that the biases are small, at most around 0.2 per cent per annum, but less than this for most of the sample. On average, in the EU the bias is slightly positive but seems to show no particular trend. Turning to the US, there is a hint of a J-curve effect after the mid-2000s. The bias, which was almost 0.4 per cent per annum fell steadily to around -0.2 per cent per annum in 2011, with the spike in the financial crisis years which presumably reflects mismeasured utilization and the like. Since then, the bias has been moving back towards being positive. This then suggests that the pre-crisis TFP growth slow-down, which has been noted by a number of authors, may be somewhat overstated. That said, the effects do not appear to be all that large...."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 5


"...What is the intuition behind this apparent finding that the positive ‘swoosh’ of the ‘J’ appears quite quickly?We can get some insight into this from Figure 13, which shows the different components of the mismeasurement on the right-hand side of equation (4). These are the capitalization effect, the errors-in-shares effect, and the mismeasured capital payments effect. What is notable is that the mismeasured capital payments effect comes in very strongly and quite quickly. The intuition here would seem to be the following. As set out in equation (3), intangibles depreciate quite quickly. That means that a burst in intangible investment rapidly builds up the intangible stock, and the rental price on that stock is relatively large (the per-period rental price of capital has to be large for capital that depreciates to compensate the capital owner for renting out an asset whose value will fall quickly). As a result, the missing rental payments effect comes in very quickly following even unmeasured intangible investment. That means that the second half of the ‘J’ appears rather quickly after the initial dip. All this suggests that investments in artificial intelligence will have a more substantial ‘J effect’ the more they are mismeasured and the lower their depreciation rate. Now, it might be that current AI investments are not substantially mismeasured for the simple reason that we might still be too early in the AI investment cycle. As the earlier graphs showed, the ‘second wave’ of AI might be comparatively recent and so it might be too early to pick up the mismeasurement involved. It might also be that we have mismeasured depreciation rates. At the moment, in our system AI-related investments are given the high depreciation rates the literature has found are appropriate to intangibles. But it may, of course, be the case that artificial intelligence investment has a much lower depreciation rate...."

Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio: Extended Excerpt Image 6


Carol Corrado, Jonathan Haskel and Cecilia Jona-Lasinio, "Artificial intelligence and productivity: an intangible assets approach," Oxford Review Of Economic Policy, 2021, https://academic.oup.com/oxrep/article/37/3/435/6374681

  • Productivity
    • Intangibles
    • Investment
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Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
Date Posted:
September 4, 2026
Is Database:
Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

Related Articles:

  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…
  • How Students and Recent Grads are Responding to the Rise of AI — Far from shying away from AI, American undergraduates “are flocking towards the most-AI-exposed degrees,” with enrollment in these majors up 8% last year…
  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
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Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

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  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
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The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

Related Articles:

  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
  • Innovation/Research
  • Productivity
    • Investment

Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

Chad Syverson Economic Innovation Group
Date Posted:
August 28, 2026
Is Database:
Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Core argument: U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup® AI

  1. U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
  2. The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
  3. Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

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  • AI and Productivity — Rising US labor productivity is driven by higher capital utilization—factories, servers, and hotel rooms running harder—rather than new investment or efficiency gains at the individual task level.
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  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — Event studies indicate that adoption of AI coding tools raised “commits” (saved code updates) ~180%, but releases by only ~30%. Large upstream…
  • Investment
  • GDP
    • Growth
  • Productivity
    • Innovation/Research

US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

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  • Investment
  • Comparisons
    • Europe USA Relative Performance
  • GDP
    • Growth
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    • Innovation/Research

Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

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